Modeling and Simulation of Urban Transport Networks: Six Cases in Lima
Studies of urban transport networks in many cities are not well focused, since they do not include tools for their planning and control, making decisions to solve the multiple problems regarding traffic congestion expensive and not adequate, generating discomfort in the users, and many times it aggr...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Perú |
| Institución: | Universidad Nacional Mayor de San Marcos |
| Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
| Idioma: | español |
| OAI Identifier: | oai:revistasinvestigacion.unmsm.edu.pe:article/19391 |
| Acceso en línea: | https://revistasinvestigacion.unmsm.edu.pe/index.php/rpcsis/article/view/19391 |
| Access Level: | acceso abierto |
| Palabra clave: | Model transport network simulation traffic congestion Modelos red de transporte simulación congestión vehicular |
| Sumario: | Studies of urban transport networks in many cities are not well focused, since they do not include tools for their planning and control, making decisions to solve the multiple problems regarding traffic congestion expensive and not adequate, generating discomfort in the users, and many times it aggravates the problems, since its impact is not evaluated. Currently exist a variety of simulators that are helpful for traffic simulation and online monitoring. An alternative to this are traffic simulators, which currently exist in variety, however, they are not easily accessible, a formal study and a relationship with the research center that provides it is required, their personalized reports are limited and they comply to another reality. In the present work, six frequent cases of urban transport networks in Lima are modeled, simulated and validated: intersection, oval, union, by-pass, clover and T; by using Arena (general-purpose simulator), known statistical and simulation techniques. The T model was validated with an average confidence level of 95%, in addition, personalized information could be obtained for decision making. |
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